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Databricks Certified Professional Data Scientist Exam Questions
- Topic 1: A intermediate understanding of the steps in the machine learning lifecycle/ Model training, selection, and production A complete understanding of the basics of machine learning/ in-sample vs. out-of sample data
- Topic 2: Applied statistics concepts/ bias-variance tradeoff
- Topic 3: A complete understanding of basic machine learning algorithms and techniques/ Unsupervised techniniques like K-means and PCA
- Topic 4: A complete understanding of the basics of machine learning model management/ Linear, logistic, and regularized regression
- Topic 5: Tree-based models like decision trees, random forest and gradient boosted trees/ Categories of machine learning
- Topic 6: Specific algorithms like ALS for recommendation and isolation forests for outlier detection/ Logging and model organization with MLflow
Free Databricks Databricks Certified Professional Data Scientist Exam Actual Questions
Note: Premium Questions for Databricks Certified Professional Data Scientist were last updated On Jul. 07, 2025 (see below)
Suppose A, B , and C are events. The probability of A given B , relative to P(|C), is the same as the probability of A given B and C (relative to P ). That is,
From the definition, P(A,B|C) P(B|C) =P(A,B.C)/P(C) P(B.C)/P(C) =P(A,B.C)
P(B,C) =P(A|BC)
This follows from the definition of conditional probability, applied twice: P(A,B)=(PA|B)P(B)
A bio-scientist is working on the analysis of the cancer cells. To identify whether the cell is cancerous or not, there has been hundreds of tests are done with small variations to say yes to the problem. Given the test result for a sample of healthy and cancerous cells, which of the following technique you will use to determine whether a cell is healthy?
Question-13. Which of the following is not the Classification algorithm?
Refer to exhibit
You are asked to write a report on how specific variables impact your client's sales using a data set provided to you by the client. The data includes 15 variables that the client views as directly related to sales, and you are restricted to these variables only. After a preliminary analysis of the data, the following findings were made: 1. Multicollinearity is not an issue among the variables 2. Only three variables-A, B, and C-have significant correlation with sales You build a linear regression model on the dependent variable of sales with the independent variables of A, B, and C. The results of the regression are seen in the exhibit. You cannot request additional dat
a. what is a way that you could try to increase the R2 of the model without artificially inflating it?
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